16 analytic disciplines compared to data science

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on?

Here the author of the post compares several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. He also provided typical job titles, types of analyses, and industries traditionally attached to each discipline.

Comparison with other analytic disciplines

Machine learning
Data mining
Predictive modeling
Statistics
Industrial statistics
Mathematical optimization.
Actuarial sciences
HPC
Operations research
Six sigma
Quant
Artificial intelligence.
Computer science
Econometrics
Data engineering
Business intelligence
Data analysis
Business analytics

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Why and How this Indian Realty Portal Exploits Data Science

While banks and ecommerce companies have tried their hands at data science and are benefitting from their initiatives around it, real estate appears to be the next big vertical that intends to leverage data science.

Housing.com, the Mumbai-based startup, is one among the top Indian online businesses that bet on data science and machine learning algorithms as a core priority. The realty portal, which raised $190 mn from Japan’s SoftBank, has come up with many tools such as Traffic Flux, Heat Maps, Listing Decay, and more in their efforts to present information to users in a visually appealing way that is more interactive than traditional plain listings.

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Questions From Data Science Interviews

You’ve just spent the last year working on honing your skills through the Data Analysis program. You dutifully spent your evenings — sometimes late into the night — on homework and projects, ignoring friends, family, and the ever-growing mountain of laundry in the corner. You coded hobby projects for your local municipality and wrote up an entire epic (at least, you thought it was epic) series of blog posts on your findings. And when a friend mentions her department’s struggling marketing efforts, your mind spins away on ways you might capture meaningful data and process the results.

Now you want to get a job. Which, despite your best efforts as a data analyst, depends almost entirely on acing the interview.

It should come as no surprise that careful preparation, and understanding the expectations going into the process, is what it takes not only to survive an interview for a data analyst position, but to set yourself apart as the best and most qualified candidate.

And even if you’re not actively looking for a position — if you’re still learning your craft and working your way through your projects — you can get started right now practicing interview questions, so that in six or twelve months, you’ll have done all the legwork necessary to wow your potential employers and win that coveted dream job offer.

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